20.500.12556/DKUM-97576 Community detection framework based on 3D shape descriptors for tree species classification in point cloud data Accurate tree species classification from remote sensing data, such as LiDAR point clouds, is important for various applications, including vegetation monitoring and forest growth prediction. Although numerous machine learning algorithms are used widely for these tasks, several challenges remain. These include the need for extensive training datasets, over-fitting to specific geographic areas and environmental conditions during tree growth, the requirement for post-processing adjustments, and unreliable performance with rare tree species and shapes. This paper presents a robust framework for classifying tree species directly from diverse point cloud datasets, eliminating the need for training machine learning models or manually preparing the training datasets. The proposed approach performs community detection on a graph using features extracted from point clouds of individual trees. Multiple shape descriptors are proposed as feature vectors that consider 3D tree crown structure and are rotationally invariant. Community detection groups the trees into distinct communities based on these feature vectors. Tree species classification is performed by classifying these communities, reducing the manual effort significantly, as only a few trees from each community need inspection. The proposed framework was validated using both real-world terrestrial LiDAR and synthetic point clouds. The results demonstrate its competitiveness with established methods, while surpassing the performance of traditional clustering techniques applied to the same feature vectors. Additionally, the results confirm the effectiveness of the proposed feature vectors for achieving competitive tree species classification accuracy. tree species classification community detection algorithm remote sensing point clouds feature vectors elagitanini spektroskopija true false true Nature Publishing Group https://www.nature.com/srep/ Angleški jezik Slovenski jezik Članek v reviji 2026-03-20 12:09:22 2026-03-20 12:09:22 2026-07-18 03:13:00 0000-00-00 00:00:00 2026 0 0 30 str. Published: 04 March 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-05-30 2026-02-25 2026-03-04 004.9 272131331 10.1038/s41598-026-42392-4 2045-2322 s41598-026-42392-4.pdf s41598-026-42392-4.pdf 1 F5110A7BD69B2B4B39730A07FF7104E1 01b78c21e391f359fd1e66ff64a43bcbba05e1c789fee2c96d00b7b8e00f6d0f ac1640b1-81c0-11f1-a6d3-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=208362 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0